This Jupyter Notebook serves as a cardiovascular disease (CVD) prediction model developed using Python and popular data analysis libraries such as Pandas, Numpy, and Seaborn. The model takes input data representing a patient's symptoms and utilizes the K-Nearest Neighbors (KNN) algorithm to predict whether the person is likely to have cardiovascular disease or not.
- Input: The notebook takes various symptoms of patients as input, such as age, blood pressure, cholesterol levels, etc.
- Algorithm: Utilizes the K-Nearest Neighbors (KNN) algorithm for prediction.
- Libraries: Built using Python and common data analysis libraries including Pandas, Numpy, and Seaborn.
- Visualization: Seaborn is used for data visualization, providing insights into the dataset and model performance.
- Clone the Repository:
git clone https://github.com/your-username/your-repository.git